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Title: An automated near-infrared system for selecting individual kernels based on specific quality characteristics

Author
item Dowell, Floyd
item Maghirang, Elizabeth
item Graybosch, Robert
item BAENZIGER, P - UNIV OF NEBRASKA
item BALTENSPERGER, DAVID - UNIV OF NEBRASKA
item Hansen, Lavern

Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2006
Publication Date: 9/1/2006
Citation: Dowell, F.E., Maghirang, E.B., Graybosch, R.A., Baenziger, P.S., Baltensperger, D.D., Hansen, L.E. 2006. An automated near-infrared system for selecting individual kernels based on specific quality characteristics. Cereal Chemistry. Vol. 83(5):537-543.

Interpretive Summary: There is currently no method to select kernels with specific end-use characteristics to assist breeders in developing cultivars for specific grower needs or for specific markets. Current methods of developing new cultivars require many years of repetitive crosses to attempt to develop pure lines with specific traits. In this research, we developed a system that can automatically select specific kernels with specific traits from populations. The system utilizes near-infrared spectroscopy that measure attributes such as protein content, starch levels, or kernel hardness in individual kernels, and then removes those kernels from the sample at a rate of about 1 kernel/2 s. These kernels can then be used by breeders to develop cultivars with specific traits that will result in crops with improved agronomic performance and improved end-use quality. Also, the selection of kernels can occur in a few minutes and does not require years of crossing required in current breeding programs. The system can also be used to measure the variability of quality within samples, providing valuable information to grain handlers, storage managers, millers, and grain processors. The system has been applied to wheat and proso millet, and could apply to other grains.

Technical Abstract: An automated system was developed that nondestructively measured quality traits of individual kernels, then sorted the kernels based on user-defined criteria. After calibrations were developed and installed, the system sorted samples with little user intervention or expertise. The system was applied to sorting wheat, Triticum aestivum L, kernels by protein content and hardness. Single kernels with high protein content could be sorted from pure lines so that the high-protein content portion had a protein content of 3.1% greater than the portion with the low-protein content kernels. Likewise, single kernels with specific hardness indices could be removed from pure lines such that the hardness index in the sorted samples was 29.4 hardness units higher than the soft kernels removed from that sample. It was also used to sort proso millet, Panicum miliaceum L., into amylose-bearing and amylose-free fractions. The system was able to increase the waxy, or amylose-free, kernels in waxy samples from 94% in the unsorted samples to 98% in the sorted samples. The waxy kernels in segregating samples were increased from 32% in the unsorted samples to 55% after sorting. Thus, this technology can be used to enrich the desirable class within segregating populations in breeding programs, to increase the purity of heterogeneous advanced or released lines, or to measure the distribution of quality within samples during the marketing process.